SiLRTC [1]} and HaLRTC [1]} assume that the original, un-corrupted tensors are low-rank. They iteratively reduce the rank of the damaged tensor via singular value shrinkage to try to recover the original tensor values. Due to their iterative operation, these general tensor completion methods introduce significant processing delays, which is a problem in applications sensitive to inference latency. SiLRTC and HaLRTC make no assumption about the spatial distribution of missing values in a corrupted tensor, or how the tensor was created in the first place. In [3]}, the Frobenius norm of the difference between completed tensors over successive iterations of tensor completion with SiLRTC and HaLRTC was found to have reached a plateau within 50 iterations. Therefore, we ran SiLRTC and HaLRTC for 50 iterations in our experiments.
